All of AI's New Models and Tools

0:00Today on the AI Daily Brief, all of AI's new models and tools, and before that in the headlines, One model that you're not getting, apparently, is OpenAI's forthcoming spud. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

0:23All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Blitzy, Zencoder, and Drada. To get an ad-free version of the show, go to patreon.com slash ai daily brief, or you can subscribe on Apple Podcasts. If you want to learn more about sponsoring the show, send us a note at sponsors at aidailybrief.ai. While at aidailybrief.ai, you can also find the link to our March AI Usage Pulse Survey. I'll have this open for a couple more days and would so appreciate you taking a couple minutes to do it. It allows us to share better data around how usage patterns in AI are changing, which is something that I think can be really valuable for people.

0:59You can also find more information on the website about things like our newsletter, which is officially back and has all the links from every day's show, or you can find links to related experiences like Enterprise Claw, which is basically the enterprise-grade version of Arc Free Claw Camp that's supported and led by Nufar Gaspar. Registration for that is closing at the beginning of next week, so check it out at enterpriseclaw.ai. Open AI obviously could not let Anthropic have all the fun when it comes to models too powerful to release to the general public. On Thursday morning, Axios reported that Open AI also plans a staggered rollout of their new model because, once again, of the cybersecurity risk.

1:36Now, this is just from one source, but it isn't all that surprising to see. Certainly, it doesn't seem to be surprising the denizens of AI Twitter, and some think that this is a forced response to Anthropic. Writes Daniel Mack, Breaking! OpenAI will not release Spud. The information reported just a few weeks ago that it was set to be released, quote, in a few weeks. Greg Brockman talked about it on the Big Technology podcast. Dario forced their hand. Total Anthropic victory. Leo SynthwaveDD simply says, lol. Dax from OpenCode writes, This was already a thing since at least GPT 5.3, but now we have to suffer a cycle of confusing mystery and go through this whole, well, it was BS last time, but maybe this time is different.

2:13We're all just caught between these two companies. I think Dan Shipper nails it when he writes, The new status symbol is making a model so powerful you can't release it. Here's something I haven't had to do often. Turns out that we actually got more on Spud almost immediately after I finished recording. Dan Shipper just tweeted, The Axios story floating around about OpenAI limiting the release of their newest model SPUD isn't true. Just spoke to OpenAI and it appears the story conflated two things. They do have a cyber product they are testing with a trusted tester group, but this is not the same thing as SPUD.

2:44The Axios story has now been updated. My friends, we are playing with live ammunition here, but since I caught this in time to update, I wanted to make sure we did. Let's move on to our next story about Perplexity Computer. In our show about how every AI product is turning into every other AI product, We covered Perplexity's computer and the general open-clawification of the AI world. Based on Perplexity's financial results, it seems to be working. Between the combination of shifting to usage-based pricing and the launch in February of computer, the company's revenue effectively doubled in a single quarter.

3:15The Financial Times reported that the company has 100 million monthly active users, tens of thousands of enterprise clients, and 450 million in ARR. Chris Brown from Inspired Capital writes, Perplexity back in the race with a single product launch is like a baseball team batting around the order twice and putting up 10 runs in the sixth inning. Interestingly, one of the sub-themes that you can see a lot on Twitter slash X is that the finance space in particular seems to be really into Perplexity Computer. Geiger Capital writes, Perplexity launched their AI agent computer a month ago and their revenue has immediately gone parabolic.

3:46AI demand is still accelerating. Nobody is ready for the compute we need. Still others remain skeptical. Kyle Russell writes, I do not consider this back in the race. Insane product fit for self-driving computers pulling them up, but Cowork and GPT SuperApp will mog this. In more evidence of just how much these types of use cases are growing, GitHub appears to be straining under the pressure of the agentic coding wave. Now, as capabilities have increased, it has led to an explosion in the amount of code being written, and it appears that that is nowhere more obvious than in GitHub's metrics. Last year, GitHub celebrated a huge expansion with Vibe Coding allowing first-time coders to come online.

4:23GitHub saw 1 billion code commits throughout the year for the first time. This year, GitHub is seeing 275 million commits per week, putting them on track for 14 billion commits by the end of the year at the current pace. And the numbers are still climbing. GitHub COO Kyle Daigle said, Since January, every month, every week almost now has some new peak stat for the highest usage rate ever. And while Daigle attributed the change to both agents and humans, it's clear that AI-enhanced coding is behind the massive increase in throughput. Commits to public repos from clawed code have swelled 25x in the past six months, reaching 2.5 million last week.

4:58Now, unfortunately, the surge in the amount of code being pushed is revealing limits in GitHub's infrastructure. Outages are becoming more frequent, and many are expressing issues with the platform. OpenClaw creator Peter Steinberger complained last week, I keep hitting quota limits from GitHub's API. This hasn't been designed with agents in mind. Kyle Daigle responded to these types of concerns, saying that GitHub is, quote, pushing incredibly hard on more CPUs, scaling services, and strengthening their core features. For now, it is just one more piece of evidence around how things are changing and how quickly.

5:28Lastly today, Anthropic has lost the second round of their legal battle against the Pentagon as the case gets more convoluted. On Wednesday, a federal appeals court in D.C. denied Anthropic's application to suspend their supply chain risk designation pending a full hearing. The three-judge panel wrote in their order, In our view, the equitable balance here cuts in favor of the government. On one side is a relatively contained risk of financial harm to a single private company. On the other side is judicial management of how and through whom the Department of War secures vital AI technology during an active military conflict.

6:00Now, the order did recognize the urgency of the case, and the court has scheduled oral arguments for mid-May. The court also acknowledged that Anthropic is likely to, quote, suffer some irreparable harm as a result of the case. Now, you might recall that Anthropic was granted an injunction from a California court early in March. Importantly, there's actually two separate lawsuits going on, dealing with two separate legislative powers invoked by the government. The California injunction means that non-Pentagon government agencies don't need to cancel contracts with Anthropic. The new ruling deals with the Pentagon exclusively and allows them to treat Anthropic as a supply chain risk.

6:33What's less clear is how military contractors in the private sector are supposed to deal with Anthropic, as both lawsuits deal with that issue to some extent. Roger Parloff, the senior editor at Lawfare, shared his view that for the moment, government contractors can probably use Anthropics technology for anything but covered government contracts. He also noted that Anthropics models have already been restored to USAI.gov, the central platform served by the General Services Administration. Importantly, this was just a preliminary ruling that has a very high bar for success, so is not necessarily a strong indication on how the case will ultimately resolve.

7:05Acting Attorney General Todd Blanche called the ruling a resounding victory for military readiness. He wrote, Our position has been clear from the start. Our military needs full access to Anthropics models if its technology is integrated into our sensitive systems. Military authority and operational control belong to the commander-in-chief and department of war, not a tech company. An Anthropics spokesperson, meanwhile, said, We're grateful the court recognized these issues need to be resolved quickly and remain confident the courts will ultimately agree that these supply chain designations were unlawful.

7:34In understated fashion, Matt Shruers, the chief executive of the Computer and Communications Industry Association commented, the D.C. Circuit's denial will prolong ambiguities regarding whether political considerations can drive federal procurement. Charlie Bullock, a senior research fellow at the Institute for Law and AI, told the information he was unsurprised by the result, noting, two out of the three judges on the D.C. Circuit panel have been very, very sympathetic to the Trump administration's aggressive claims about executive authority in the past. Expanding his analysis on X, Bullock noted that the case is moving quickly and could receive a final order within six weeks.

8:06Now, even if they fail to convince the panel, Anthropic could appeal to the full DC Circuit, which is majority Democrat, and also have the timing right to get their case on this year's Supreme Court docket in the fall. Bullock predicted Anthropic would probably succeed at the Supreme Court, commenting, the dynamic here is not left versus right, it's cares about the law at least a little bit or doesn't like the administration versus does not care about the law at all and likes the administration. Now, how, if at all, the revelations about the power of anthropics mythos impact this remains to be seen, but for now, that is going to do it for the headlines.

8:36Next up, the main episode.

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12:27Welcome back to the AI Daily Brief. One would be forgiven for thinking that this week has been defined by models that we actually didn't have access to. A huge part of the discourse throughout the week has of course been about Anthropik's mythos, a model which it found too powerful to release in the normal way that it had been, and which right now is only in the hands of about 40 partners for some very limited cybersecurity-focused engagement. Then just this morning, as you heard in the headlines, we also heard that OpenAI planned its own staggered rollout of their new model for similar reasons, cybersecurity risks.

12:57Now, even among people who understand theoretically why these companies are doing this, there's still, I think, a bit of a sentiment of don't tell me about the new toys if I can't play with them. But luckily, the rest of the AI industry is not slouching at all. And in fact, even Anthropic themselves have given us something different that's still pretty powerful to play with. So let's talk through all of the other models and tools that have been released, starting with the first big model release from the new Meta Superintelligence Lab. Muse Spark is Meta's first new model release in over a year.

13:28It's also the first model to come from the new Meta Superintelligence Labs division, which is of course the collection of superstar, crazy high-paid AI researchers that was put together last summer and brought together under the leadership of Alexander Wang, who was brought in through the$14 billion-plus partial acquisition of his company, Scale. MuseSpark will be the first of the Muse family of models, with Meta ditching the llama name and associated baggage. The Muse models are natively multimodal reasoning models, similar to Google's Gemini architecture. Meta noted that they support tool use, visual chain of thought, and multi-agent orchestration.

14:00Now, those features are at this point kind of table stakes for the current generation, but based on fairly low expectations, people were still encouraged to see them present here. Meta didn't indicate how large the model is, or whether it uses a mixture of experts' architecture. In fact, we don't really know at all where this model sits in the model family. Executives referred to it as small and fast, but its performance in comparison points looked closer to a mid-sized or large model. On the benchmarks, at first glance, MuseSpark looks pretty capable. It scored 52.4 on Sweebench Pro, for example, putting it within a few points of Opus 4.6, Gemini 3.1 Pro, and GPT 5.4 for coding.

14:37On Humanity's last exam, it scored 42.8, which is slightly better than Opus, but trailing Gemini and GPT 5.4. Now, interestingly on that one, with tools enabled, Muse's score only jumped to 50.4, leaving it trailing all three of those major rivals by a few points. This could suggest the model isn't as good at web search or tool use as the others, but of course, this is only a single data point. The general sense you get from the benchmarks is that Muse is in the mix, but certainly not leading the pack. And you can certainly tell where Meta is trying to put the emphasis. Rather than leading with their scores on Humanity's Last Exam or SweetBench, those scores are buried fairly deep in the results table, with Meta instead leading on the multimodal benchmarks where MuseSpark excels.

15:18The model scored 86.4 on Charvik's reasoning, which is a measure of visual comprehension, which would actually have that being a state-of-the-art result, beating Gemini 3.1 Pro by 6 points. MuseSpark did slightly trail Gemini on assortment of other visual tests, but the results were strong enough to suggest the model will be highly capable. Now, these benchmarks also gel with how Meta views the model's purpose. Unlike the other model companies where there is increasing focus on coding use cases and enterprise use cases more broadly, MuseSpark is designed primarily to drive personal agents. In a Threads post, Mark Zuckerberg wrote that MuseSpark is a world-class assistant and particularly strong in areas related to personal superintelligence like visual understanding, health, social content, shopping, games, and more.

16:01And interestingly, in that same note, while Zuckerberg is trying to draw a clear differentiation between the work-focused use cases the other companies are pursuing, there is still broadly, even here and even in the personal realm, a shift from assistant AI to agentic AI. Zuckerberg ends his Threads post by saying, We are building products that don't just answer your questions but act as agents that do things for you. Giving more examples of where these capabilities will be useful, Meta wrote, that they enable interactive experiences like creating fun minigames or troubleshooting your home appliances with dynamic annotations.

16:30The model will immediately go into service driving Meta AI and will presumably arrive across their social media platforms over time. MuSpark will function in three modes, instant with no reasoning, thinking mode which enables reasoning, and contemplating mode that performs deep research style multi-step reasoning. Contemplating mode, however, won't be available at launch. Meta also emphasized the health assistant use case, touting that they collaborated with a thousand physicians to curate training data for factual accuracy. Now, in this case, there doesn't seem to be a separate interface for health, it's just functionality that's being encouraged on Meta's existing platforms.

17:02Meta AI leader Alexander Wang argued that MuseSpark is just the beginning, posting, This is step one. Bigger models are already in development with infrastructure scaling to match. Private API preview open to select partners today, with plans to open source future versions. One strand of the response that's been fairly consistent was basically, welcome back to the party, guys. To some, even though this model is clearly behind the other leaders, the fact that the Meta Superintelligence lab was able to get it out in less than a year since that lab was formed was a feat in and of itself. Others were just less impressed.

17:33Ethan Mollick writes, after playing with it a bit, Meta's Muse Spark thinking is fine so far, but really doesn't match the current big three models. It is also a bit weird. Like some strange language and tone, a little loose with facts, etc. After giving a few examples, he concludes, Anyhow, it's not bad, just not the vibe level that the benchmarks might indicate. And for a first re-entry into the frontier model space, given the engineering efficiencies they achieved, it feels like a solid attempt. I'm sure we will see better from Meta in the future. ARC Prize founder Francois Chalet was less forgiving.

18:03He wrote, The new model from Meta is already looking like a disappointment, over-optimized for public benchmark numbers at the detriment of everything else. Knowing how to evaluate models in a way that correlates with actual usefulness is a core competency for AI labs, and any new lab is unlikely to be successful without first figuring that out. Wang actually decided to respond to that one, saying, We're always open to feedback and welcome any perspective on weaknesses you've noticed in the model from using it. We're quite upfront that our model does not perform well on ArcGi2, for example, and publish those results for the community to understand.

18:33That might reflect some areas of improvement of the model that we could focus on in the future. In general, though, Wang reports, We have been pleasantly surprised by users' feedback on the model in areas like visual coding, writing style, and reasoning queries. Voss on Twitter, who previously did work on Meta AI, said, Meta's latest model, MuseSpark, is actually much better than I had expected. Is it benchmark maxed? Yes, 100%. But so is every other model. Is it frontier leader in any single category? No. Is it better than I expected? Yes. I look forward to the eventual open source version. Feels like they're coming back to life.

19:04Never fade Zuck. Now, speaking of open source, another model that we got this week that got completely overshadowed by the Mythos announcement, was Z.ai's GLM 5.1. And at least on the benchmarks, it's the first open-source model to overtake leading Western models on coding benchmarks. The new frontier model, which like I said is called GLM 5.1, achieved a 58.4 on SWE Bench Pro, beating GPT 5.4 and Opus 4.6, who scored 57.7 and 57.3 respectively. Z.ai also provided a mixed benchmark that included TerminalBench 2.0 and NL2 repo as well, which had GLM 5.1 slightly behind the two US leaders but ahead of Gemini 3.1 Pro.

19:44Still, if those benchmarks hold, it puts GLM 5.1 in the top echelon of frontier models with a clear separation from Quen 3.6 Plus and Kimi-K 2.5. And indeed, what most people are clinging onto is the fact that this is a full open-source release with commercial licensing. It's a gigantic 754 billion parameter model, so you're not going to be running it locally on a Mac Mini, still, it gives developers the opportunity to build on top of current-generation state-of-the-art models for kind of the first time. We've been tracking the apparent shift in Chinese lab strategy away from open source recently, but this release suggests that leading Chinese labs are at least still somewhat willing to give away their best-performing models.

20:19In terms of performance, ZAI provided a few impressive examples in agents encoding. They claim that GLM 5.1 spent eight hours autonomously building a Linux desktop using a self-review loop to remove the need for human intervention. And this is kind of what they emphasized in their announcement post as well, calling the blog post GLM 5.1 towards long horizon tasks. Running VectorDB tests, the model was capable of carrying out the database optimization test with significant results. The model carried out over 600 iterations using more than 6 ,000 tool calls to deliver 6x the performance of a standard 50-turn session.

20:50Z.ai leader Lou wrote on X, Agents could do about 20 steps by the end of last year. GLM 5.1 can do 1 ,700 right now. Autonomous work time may be the most important curve after scaling laws. GLM 5.1 will be the first point on that curve that the open source community can verify with their own hands. Now, of course, whenever a company reports their own benchmarks, it's always worth taking it with a grain of salt and waiting to see what the actual vibes are around it as people get their hands on it. But at least at first glance, the model looks like a big step up for Chinese AI. It was trained entirely on less powerful Huawei chips, again demonstrating that the Chinese hardware stack can produce some powerful results.

21:25Also, coming just two months after the release of Opus 4.6 and GPT 5.4, it suggests the U.S. continues to be only months ahead of their Chinese rivals. Leet LLM summed up the gap in the conversation on X, saying, Everyone's freaking out about Claude Mythos, while ZAI casually open-sourced a model built for eight-hour autonomous execution. Now, speaking of Claude and Anthropic, if you thought they were going to slow down for the sake of discussion around Mythos, think again. On Wednesday afternoon, the company announced Claude Managed Agents, which they are pitching as everything you need to build and deploy agents at scale.